rbf神经网络python实现
时间: 2023-10-03 21:08:26 浏览: 60
以下是一个简单的RBF神经网络的Python实现示例:
```python
import numpy as np
class RBFNet(object):
def __init__(self, hidden_size, sigma=1.0):
self.hidden_size = hidden_size
self.sigma = sigma
self.centers = None
self.weights = None
def _gaussian_basis(self, X, center, sigma):
return np.exp(-np.linalg.norm(X - center, axis=1)**2 / (2 * sigma**2))
def _kmeans(self, X):
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=self.hidden_size).fit(X)
return kmeans.cluster_centers_
def fit(self, X, y):
self.centers = self._kmeans(X)
G = np.zeros((X.shape[0], self.hidden_size))
for i in range(X.shape[0]):
for j in range(self.hidden_size):
G[i, j] = self._gaussian_basis(X[i], self.centers[j], self.sigma)
self.weights = np.dot(np.linalg.pinv(G), y)
def predict(self, X):
G = np.zeros((X.shape[0], self.hidden_size))
for i in range(X.shape[0]):
for j in range(self.hidden_size):
G[i, j] = self._gaussian_basis(X[i], self.centers[j], self.sigma)
return np.dot(G, self.weights)
```
这个实现使用K-Means算法来确定RBF神经网络的中心点,然后使用高斯函数作为基函数来计算每个中心点对输入数据的响应。最后使用线性回归方法来计算权重矩阵,用于预测新的数据点。
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